Author

Date Thesis Awarded

Document Type

Honors Thesis

Degree Name

Bachelors of Science (BS)

Department

Biology

Advisor

Matthias Leu

Committee Member

Oliver Kerscher

Committee Member

John P. Swaddle

Committee Member

David P. Aday

Abstract

Amblyomma americanum, or the lone star tick (LST), is the most prevalent tick in the southeastern United States, and is known to transmit the bacterium Ehrlichia chaffeensis, responsible for the disease, human monocytic ehrlichiosis (HME). Despite the LST's prevalence and connection with disease, little research has been published explaining LST spatial variation on a regional scale. The objectives of this study were to determine prevalence of Ehrlichia chaffeensis and identify factors influencing LST distribution across the Virginia peninsula. I sampled ticks at 101 random sites stratified along an urban-rural gradient in 2010 and 2012. I counted ticks at each site along two 30- m transects and when possible, collected up to 20 nymph ticks per site for laboratory analysis. Nucleic acid was extracted from pooled sites of up to 20 nymph ticks in 2010 and 2012. Polymerase Chain Reaction was used to amplify DNA coding for 16s rRNA unique to E. chaffeensis. Amplicons were observed at a total of eight sites. Though bacterial prevalence was too low to model, the results of this study indicated the importance of determining variables that best predict LST density in order to minimize the risk of human contact with LSTs carrying disease. The nymph LST counts were modeled using a count-based regression analysis. A hierarchical information-theoretic modeling process was used to determine best predictor variables, which were selected using Akaike's information criterion corrected (AICc). The top models were averaged into a final model and then spatially applied in Geographic Information Systems (GIS). My study indicated that the proportion of mesic oak forest at the 300-m scale was the most important positive predictor of lone star nymph count, followed by deer habitat use measured at the sampling scale, and the edge density of mesic oak forest and other land cover patches at the 240-m scale. The strongest negative predictor was the proportion of early successional land cover at the1600-m scale, followed by the decay distance to forest edge, and ground feeding bird density. The results of this project provide an opportunity to employ bio-informed land management in the Virginia Peninsula in order to decrease the number of lone star nymphs and minimize the public risk of contracting E. chaffeensis bacteria.